code: def forward(self, verts3d, points2d, affine):
      # pred_2d_lmks  Batch*N*2
      # verts Batch*N*3
      ones = torch.ones(
          [points2d.shape[0], points2d.shape[1], 1],
          device=points2d.device,
      )
      verts_homo = torch.cat((points2d, ones), 2)
      K = eye_like(affine, 3)
      K[:, :2, :3] = affine
      inv_k = torch.linalg.inv(K)
      inv_k @ verts_homo.permute(0, 2, 1)
      points2d_inv = inv_k @ verts_homo.permute(0, 2, 1)
      points2d_inv = points2d_inv.permute(0, 2, 1)[:, :, :2]
  
      intrinsics = self.intrinsics.repeat([verts3d.shape[0], 1, 1])
      # print(verts3d.double().shape)
      # print(points2d.double().shape)
      # print(intrinsics.shape)
      RT_ = kornia.geometry.solve_pnp_dlt(
          verts3d.double(),
          points2d_inv.double(),
          intrinsics,
          svd_eps=self.eps,
      )
      RT_ = RT_.float()
      RT = eye_like(verts3d, 4)
      #         RT[:,1:3,:] *=-1
      RT[:, :3, :] = RT_
      RT = RT.permute(0, 2, 1)
      RT[:, :, :2] *= -1
  
      ones = torch.ones(
          [verts3d.shape[0], verts3d.shape[1], 1], device=verts3d.device
      )
      verts_homo = torch.cat((verts3d, ones), 2)
      M = self.M.repeat([verts3d.shape[0], 1, 1])
      verts = verts_homo @ RT @ M
      w_ = verts[:, :, [3]]
      verts = verts / w_
      reproject_points2d = verts[:, :, :2]
      loss = self.loss_fn(reproject_points2d, points2d_inv)
  
      return loss
  
file_path: insightface/reconstruction/jmlr/losses.py
imports: import torch
  from torch import nn
  import torch.nn.functional
  import kornia
  import numpy
label_desc: Use image node_id 2 for calling the ProjectLoss obj's underlying member method code with example usage: obj.forward(verts3d, points2d, affine) and returns: loss